system

Abstract

We provide the system. [Solution] A means of inputting information for entering the user's health information, An advice generation method that analyzes input health information and generates personalized drug prescription advice using generative artificial intelligence, An advice provision method that provides the generated advice to the user, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the progress of an aging society, many people need appropriate advice on taking medicine based on their health conditions, but there is a current situation where it is difficult to visit a medical institution easily. As a result, there is a problem that the lack of knowledge about medicine and incorrect medication schedules of users cause an increase in health risks. In response to this problem, a system that allows users to safely and surely manage the taking of medicine at home is required.

Means for Solving the Problems

[0005] To solve this problem, the present invention includes an information input means for inputting the user's health information, and collects data including the user's medical history and allergy information. Furthermore, it includes an advice generation means that analyzes this data using generative artificial intelligence and generates medication prescription advice tailored to each individual user. The generated advice is presented to the user by an advice provision means and guided in an easy-to-understand manner using voice or visuals. In addition, it includes a schedule management means that manages the medication schedule based on the generated advice and notifies the user, and a data update means that collects feedback from the user and updates the database, thereby enabling continuously accurate support.

[0006] "Information input means" refers to a device or system for users to input their own health information, medical history, and allergy information.

[0007] The "advice generation method" is a function that generates personalized medication prescription advice using generative artificial intelligence based on collected user health-related data.

[0008] "An advice provision means" refers to a device or function that provides users with prescription advice for generated medications in an easily understandable format.

[0009] The "schedule management method" is a function that manages the user's medication schedule based on the generated medication advice and provides notifications at the appropriate time.

[0010] A "data update mechanism" is a function for collecting user feedback and additional information to update the database. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention relates to a medication advice system using an AI robot installed in the home. This system supports appropriate medication management by providing personalized medication advice based on the user's health information.

[0033] 1. User interface operation:

[0034] Users can access the system through their devices and input their health information, medical history, and allergy information. This includes voice input and text input using a touch panel.

[0035] 2. Analysis of health data:

[0036] The user's health information entered from the terminal is collected by the server. The server analyzes this information and uses generative artificial intelligence to generate optimal medication prescription advice for the user. This advice includes appropriate dosage, warnings about side effects, and risks of drug interactions.

[0037] 3. Providing advice:

[0038] The generated advice is sent from the server to the terminal. The terminal displays the advice to the user and provides audio explanations as needed. This allows the user to receive clear instructions on their own device.

[0039] 4. Managing medication schedules:

[0040] Based on a medication plan generated by the server, the device manages the user's medication schedule. As the time to take medication approaches, the device prompts the user to take their medication with voice and visual alerts.

[0041] 5. Feedback and data updates:

[0042] After taking medication, users input changes in their physical condition and new health information into their device and send it to the server. The server updates the data based on this feedback and incorporates it into future advice. This cycle ensures that the system always provides the latest and most personalized support.

[0043] Specific example:

[0044] For example, if a user suffering from high blood pressure uses the system, they input their medical history and current medications. The server analyzes this information and suggests new medications and dosages. Furthermore, based on the advice, it creates a medication schedule tailored to the user's schedule, such as before breakfast or before bed, allowing them to easily integrate their medication into their daily life.

[0045] This system supports users in taking medications tailored to their individual health conditions, thereby contributing to their overall health maintenance.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The user enters their health information into the terminal. Input methods include voice input or text input via a touchscreen. The terminal converts this information into text data and then into a format suitable for transmission to the server.

[0049] Step 2:

[0050] The server receives the user's health information sent from the terminal. The server stores this information in a database and prepares it for analysis. Simultaneously, it also performs encryption to protect privacy.

[0051] Step 3:

[0052] The server uses stored information and generative artificial intelligence to generate prescription advice tailored to the user. This includes selecting medications that take into account the user's medical history and allergy information, as well as warnings about side effects.

[0053] Step 4:

[0054] The server sends the generated advice to the terminal. The terminal prepares to present the received information to the user. The information can be displayed not only on the screen but also through voice guidance.

[0055] Step 5:

[0056] The device creates a medication schedule based on the generated advice. It sets medication times that fit the user's lifestyle and provides a function to notify the user of the schedule.

[0057] Step 6:

[0058] Users can take medication according to the advice given and report any changes in their physical condition or side effects they experience after taking it to their device. This provides feedback information that helps improve the quality of future advice.

[0059] Step 7:

[0060] The terminal processes user feedback information and sends it to the server. The server reflects this information in its database and updates the data for use in generating advice the next time. This cycle ensures that the system always reflects the latest information and can provide highly accurate advice.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] In modern society, personalized health management is in demand, but traditional methods have the challenge of not being able to properly prescribe medications and manage schedules according to the individual user's information. This problem is particularly serious for people who use a wide variety of medications, as incorrect use can lead to health problems.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes information acquisition means for inputting the user's physical condition information, instruction generation means for analyzing the input physical condition information and generating personalized drug prescription instructions using generative intelligence, and instruction presentation means for presenting the generated instructions to the user. This makes it possible to provide the user with the most suitable drug prescription and appropriate medication management.

[0066] "Information acquisition means" refers to a device or method for inputting information about a user's physical condition.

[0067] "Instruction generation means" refers to a device or method for analyzing input physical condition information and generating personalized drug prescription instructions using generative intelligence.

[0068] "Instruction presentation means" refers to a device or method for presenting prescription instructions for a generated drug to the user.

[0069] "Schedule management means" refers to a device or method for managing a user's medication schedule based on the prescription instructions for the generated drugs and for notifying the user of the schedule.

[0070] "Information update means" refers to a device or method for collecting additional information and opinions from users and updating the memory area.

[0071] "Generative intelligence" is an artificial intelligence technology that has the ability to generate optimal drug prescription instructions based on input data.

[0072] This invention relates to a drug administration instruction system using an artificial intelligence-equipped device installed in the home. This system provides personalized drug instructions based on the user's physical condition information, supporting appropriate medication management.

[0073] Users can access the system through terminals equipped with voice input or touch panels. The terminals collect information on the user's physical condition, medical history, and allergies, and send this information to the server. The server analyzes this data and uses a generative AI model to generate medication recommendations optimized for each individual user.

[0074] Instructions generated by the server are sent to the terminal and provided to the user. The terminal visually displays the generated instructions on the screen, and can also provide instructions by voice if the user has difficulty accessing visual information. This allows users to receive personalized instructions based on their health condition from the comfort of their homes.

[0075] As a concrete example, a user suffering from high blood pressure enters their blood pressure readings and information about medications they are currently taking into the terminal. Based on this information, the server generates specific instructions such as "Take medication X twice a day, in the morning and at night," and presents them to the user. The terminal then issues an alert at the specified time to prompt the user to take their medication.

[0076] As an example of a prompt, you could use a text-based instruction such as, "We want to develop an AI system that takes user health information and generates personalized medication recommendations. What kind of prompts would be effective?"

[0077] In this way, the system of the present invention can efficiently perform individual health management and contribute to maintaining the health of users.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] Users input their physical condition, medical history, and allergy information using a terminal. Input methods include text input via a touch panel and voice input via voice recognition. This input data is important as basic information for understanding the user's health status. The terminal transmits this information to the server sequentially.

[0081] Step 2:

[0082] The server receives physical condition information sent from the terminal, compares it with the database, and performs analysis. Using a generative AI model, it analyzes the user's information in detail and generates personalized medication prescriptions. This generation process takes into account the input data, considering appropriate medication types, dosages, and side effect information.

[0083] Step 3:

[0084] The generated medication instructions are sent from the server to the terminal. The terminal displays the instructions visually on the screen and, if necessary, also provides them audibly. This allows the user to check medication information tailored to their needs in an intuitively understandable format. For example, instructions such as "Take one tablet of medication X after breakfast and one tablet after dinner" are output.

[0085] Step 4:

[0086] The device automatically creates and manages the user's medication schedule based on instructions sent from the server. As the designated medication time approaches, it notifies the user with an alert sound and screen notification. This feature helps prevent users from forgetting to take their medication, and provides support by notifying them with a voice message saying, "It's time to take your medicine."

[0087] Step 5:

[0088] Users input feedback into their device regarding changes in their physical condition after taking medication and any new observations they may have. This feedback information is sent to the server as data to be used in generating advice for the next time. The server updates its database based on the received feedback and incorporates it into the AI ​​model to provide more appropriate advice.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] Managing users' medication adherence presents challenges, particularly in preventing missed doses or incorrect dosages while out and about. This is especially true in public places or during travel, where users are more likely to forget to take their medication properly. Furthermore, real-time monitoring of users' health information and updating medication plans accordingly are essential. Addressing these challenges and supporting proper medication management and adherence is crucial.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes data input means for inputting the user's health information, advice generation means for analyzing the input health information and generating personalized medication prescription advice using generative artificial intelligence, data synchronization means for synchronizing health data to the cloud, and information provision means for providing the generated advice to the user. This allows the user to receive an appropriate medication schedule even when away from home, preventing medication errors.

[0094] "Data input means" refers to devices or methods for users to input their health information into a system.

[0095] An "advice generation means" is a device or method that uses generative artificial intelligence to create personalized drug prescription advice based on input health information.

[0096] "Data synchronization means" refers to devices or methods for updating and aligning health information on the cloud.

[0097] "Information provision means" refers to devices or methods that provide generated advice to users in an easily understandable format.

[0098] A "notification management system" is a device or method that manages a user's medication plan based on generated advice and provides notifications at the appropriate time.

[0099] "Information update means" refers to devices and methods for collecting additional information and feedback from users and keeping the information infrastructure constantly up-to-date.

[0100] This invention is a system for users to manage their health information and receive personalized medication advice. The server acquires health information from the user through a data input means. This health information is entered via the user's smartphone, tablet, or other device. For voice input, the system utilizes a "voice recognition API" on smart devices to convert the entered information into text in real time.

[0101] Next, the server uses an advice generation mechanism to analyze the collected health information and generates optimal medication prescription advice for the user using a generation AI model. This generation utilizes a "processing service" in a cloud computing environment, leveraging high-performance computing capabilities to provide fast and accurate prescription advice.

[0102] Furthermore, the generated advice is synchronized on the cloud and kept up-to-date at all times through data synchronization mechanisms. This information is sent to the user's device and provided to the user via audio and visual means through information delivery mechanisms. This ensures that users can always obtain accurate medication information no matter where they are.

[0103] For example, if a user is out and about and it's time to take their medication, the device will send a reminder via the notification management system. This allows the user to take their medication properly by following voice instructions, even in public places, without worrying about being seen by others.

[0104] Furthermore, post-medication feedback is sent to the cloud via an information update mechanism, keeping the database constantly up-to-date. This data is used by the server to generate advice for the next dose. An example of a prompt would be: "The user's latest blood glucose level is 150 mg / dL. Please generate advice for the next dose, taking into account meal timing and medication history." This prompt is used as input to an AI model, which generates a medication plan tailored to the individual's situation.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] Users enter health information through their device. This information includes age, gender, medical history, allergy information, and current medications. This input is performed using a "voice recognition API" or touch panel and sent to the server.

[0108] Step 2:

[0109] The server receives health information acquired through data input and generates prompts for the generating AI model based on that information. These prompts include user-specific data necessary to provide appropriate medication advice to the user. After these prompts are generated, they are sent to the generating AI model.

[0110] Step 3:

[0111] The generation AI model generates optimal medication prescription advice for the user based on the received prompts. This process produces advice that includes specific dosages and precautions tailored to the user's individual health condition and lifestyle. The generated advice is returned to the server.

[0112] Step 4:

[0113] The server synchronizes newly generated advice to the cloud. During this process, data synchronization is used to ensure all advice data is up-to-date, preparing for future advice generation and user notifications.

[0114] Step 5:

[0115] Advice sent from the server is presented to the user via the terminal's information delivery system. The terminal uses audio output and display functions to allow the user to visually and audibly confirm the advice. This step supports how the user actually uses that advice.

[0116] Step 6:

[0117] Users take their medication based on the advice they receive and then input feedback into their device. This feedback includes the actual time of administration and any changes in their physical condition. This data is sent to a server, and the database is updated using an information update mechanism.

[0118] Step 7:

[0119] The server uses the updated data to prepare for generating the next set of advice. This allows for the optimization of medication management while consistently providing users with the most appropriate medication advice.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention relates to a home-use AI robot pharmacist system that includes an emotion engine. It not only personalizes medication prescription advice based on the user's health information but also considers the user's emotional state. This system optimizes the user's medication experience by comprehensively combining information input, emotion recognition, advice generation, and advice delivery functions.

[0122] 1. User Interface and Emotion Recognition:

[0123] When users input health information through their devices, their voice or visual responses are recorded. The device analyzes these responses and recognizes the user's emotions through an emotion engine. This allows the system to understand the user's psychological state (e.g., anxiety, reassurance, stress).

[0124] 2. Analysis of health data and emotional data:

[0125] The server receives health information and emotional data transmitted from the terminal. An emotion engine analyzes the user's emotions and combines them with health information for a comprehensive evaluation. Generative artificial intelligence then generates personalized prescription advice based on this data.

[0126] 3. Providing advice that is tailored to the individual's emotions:

[0127] The server sends the generated advice to the terminal, and the terminal provides the advice in a tone and content that suits the user's emotional state. For example, a user who is feeling anxious will receive a calm and reassuring explanation.

[0128] 4. Advice and Schedule Management:

[0129] The device manages the medication schedule while considering the user's emotional state and provides emotionally sensitive notifications. For example, reminders can be customized in the user's preferred voice or visual style.

[0130] 5. Feedback and data updates:

[0131] Users input feedback into their device about changes in their feelings and emotions after taking medication. The server receives the new emotional data, updates its database, and uses it for the next analysis.

[0132] Specific example:

[0133] If a user is using the system while feeling anxious about medication side effects, the terminal detects this anxiety from the user's tone of voice and words. Based on this information, the server generates advice to reduce the risk of side effects in order to provide reassurance. The terminal then delivers this advice in a quiet and calming tone to alleviate the user's anxiety.

[0134] Thus, the present invention aims to enable flexible responses that take into account the user's psychological state and to provide a better experience regarding medication use.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] Users input health information into the device via voice or text. During this process, the device also records the user's voice tone and word choices, collecting data for analysis by an emotion engine.

[0138] Step 2:

[0139] The device sends the user's health information and emotional data to the server. The emotion engine analyzes voice tone and facial expression data to identify the user's emotional state. For example, if the user is feeling anxious or stressed, it extracts that information.

[0140] Step 3:

[0141] The server integrates and analyzes received health information and emotional data. Using generative artificial intelligence, it generates personalized medication prescription advice that takes into account the user's emotional state. This advice includes wording tailored to the user's psychological condition.

[0142] Step 4:

[0143] The server sends the generated advice to the device. The device displays or provides the advice in a tone and language that suits the user's emotions. For users who are feeling anxious, the advice is explained in a relaxed tone, demonstrating an emotionally sensitive approach.

[0144] Step 5:

[0145] Users take medication based on the advice provided. After taking the medication, they input feedback into their device and report their emotional state after taking it. This provides data to improve future advice.

[0146] Step 6:

[0147] The device organizes user feedback and sends it to the server. The server records this information in a database, uses an emotion engine to update the data, and uses it to generate advice for the next time. This process ensures that the system always provides the best possible support to the user using the most up-to-date information.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] In modern medication management systems, providing a personalized medication experience that takes into account the user's emotional state is challenging. In particular, prescription advice and medication schedules often fail to reflect the user's psychological state, resulting in insufficient reduction of anxiety and stress.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes data acquisition means for receiving information about the user's health, emotion analysis means for analyzing the user's voice and images to recognize their emotional state, and advice generation means for integrating health information and emotion data and generating personalized prescription advice using a generation AI model. This enables the provision of flexible and personalized advice according to the user's emotional state and the management of medication schedules.

[0153] "Data acquisition means" refers to devices or methods that have the function of collecting health-related information from users.

[0154] "Emotional analysis means" refers to technologies and devices for identifying a user's emotional state from their voice or images.

[0155] A "generative AI model" is a machine learning model that generates personalized results based on specific input data.

[0156] An "advice generation means" is a device or method that has the function of creating prescription advice optimized for the user using acquired health information and emotional data.

[0157] "An advice delivery method" refers to a device or method for presenting generated advice in a way that is appropriate to the user's emotional state.

[0158] A "schedule management tool" refers to a device or method that has the function of creating a medication plan for a user and notifying them in an emotionally sensitive manner.

[0159] "Data update methods" refer to technologies and devices that update databases based on user feedback to improve analysis accuracy.

[0160] This invention is a system that provides personalized prescription advice using the user's health information and emotional state. The system primarily consists of terminals and a server, and by integrating these functions, it provides the user with an optimal medication management experience.

[0161] Specifically, the device provides an interface for users to input health-related data. This includes interfaces that utilize voice and visuals. The data entered by the user is processed by the device's emotion analysis engine. In this process, voice and facial expression data are analyzed to recognize the user's emotional state.

[0162] The analyzed data is sent to a server. The server uses an emotion analysis engine and a generative AI model to integrate health data and emotion data and generate personalized prescription advice. The generated advice is sent to the device in a format that is sensitive to the user's emotions.

[0163] The device provides the user with received advice. The advice is presented in an appropriate tone using voice and visuals, designed to reduce the user's anxiety and stress. The device also manages the user's medication schedule and provides notifications that are sensitive to the user's emotional state, ensuring medication adherence.

[0164] This allows the system to provide personalized advice and plan management based on the user's emotions and health status, thereby supporting the user's well-being.

[0165] As a concrete example, if a user is feeling anxious about taking medication, the device will sense this emotion, and the server will generate specific advice to alleviate it. For example, it might offer a suggestion in a calm tone, such as, "It's a good idea to drink plenty of water when taking your medication." An example of a prompt might be, "Please evaluate the user's current emotional state based on their voice data and text analysis. If anxiety is detected, please suggest specific medication advice to alleviate it."

[0166] Thus, this invention aims to realize flexible medication management that comprehensively considers the user's psychological state and health condition.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] Users input health data using a terminal. This data includes current medication information, changes in physical condition, and medical history. This health data is used by the system as basic information to understand the user's condition.

[0170] Step 2:

[0171] The device collects voice and visual information along with the user's health data. It uses the built-in microphone and camera to record the user's facial expressions and voice tone. Using this data, the emotion analysis engine identifies the user's emotional state (e.g., anxiety, reassurance).

[0172] Step 3:

[0173] The device transmits collected health and emotional data to the server. This transmitted data is integrated on the server side and becomes input information to understand the user's overall state.

[0174] Step 4:

[0175] The server analyzes the received health information and emotional data. Based on the emotional analysis results, a generative AI model generates prescription advice tailored to the user. For example, if the user is experiencing anxiety, advice is generated that takes that emotion into account and promotes a sense of security.

[0176] Step 5:

[0177] The server sends the generated advice to the terminal. The terminal then displays the received advice to the user. In this process, speech synthesis technology and a display are used to provide the advice in a tone that matches the user's emotional state.

[0178] Step 6:

[0179] The device manages the user's medication schedule based on generated advice. A reminder function is used to notify the user to take their medication at the appropriate time. Notifications are customized according to the user's preferences.

[0180] Step 7:

[0181] Users input feedback into the device about changes in their physical condition and emotions after taking medication. This feedback serves as important data for the system to improve the accuracy of future prescription advice.

[0182] Step 8:

[0183] The server receives feedback from users and updates its sentiment database. This updated data is then used in subsequent analyses and advice generation. Through this process, the system continuously evolves, enabling it to provide more appropriate advice.

[0184] (Application Example 2)

[0185] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0186] In modern society, users often feel anxious about choosing and using medications at pharmacies. Furthermore, accurately understanding a user's psychological state and providing personalized, effective medication advice is difficult in face-to-face interactions. In this situation, there is a need for a system that can respond appropriately to users' emotions.

[0187] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0188] In this invention, the server includes an information input means for inputting the user's health information, an advice generation means for analyzing the input health information and generating personalized drug prescription advice using generative artificial intelligence, an advice provision means for providing the generated advice to the user, an emotion analysis means for analyzing the user's emotional information and providing information in an appropriate tone according to the emotion, and a display means for maintaining the user's emotional state in a face-to-face environment and providing information to the service provider. This enables accurate understanding of the user's psychological state during face-to-face service at a pharmacy and allows for personalized and effective advice.

[0189] "Information input means" refers to a device or method for acquiring health-related information collected from users as digital data.

[0190] "Advice generation means" refers to a device or method for generating individually optimized drug prescription advice using generative artificial intelligence based on the user's health information.

[0191] "Advice provision means" refers to a device or method for providing generated advice to a user in an appropriate format.

[0192] "Emotion analysis means" refers to a device or method that has the function of identifying and analyzing emotions from a user's voice, facial expressions, etc.

[0193] "Display means" refers to a device or part of a device that visually represents analyzed information or generated advice and provides it to the customer.

[0194] A system for carrying out this invention includes information input means, advice generation means, advice provision means, emotion analysis means, and display means.

[0195] The server collects health information provided by the user through an information input method. This information input method utilizes hardware devices such as smart glasses or voice input devices to acquire the user's voice and image data in digital format. The collected data is stored on a cloud server and used for analysis.

[0196] Next, the server uses generative artificial intelligence to generate personalized medication prescription advice through an advice generation mechanism. The generative AI cross-references various health databases to generate advice best suited to the user's health condition. This enables the recommendation of medications tailored to the user's specific circumstances.

[0197] Furthermore, emotion analysis techniques are used to analyze the user's emotional state from their voice and facial expression data. Specifically, existing emotion recognition software (e.g., Affectiva or IBM Watson®) can be used to understand the user's psychological state in real time.

[0198] The generated advice and emotion analysis results are provided to the service provider through display devices such as smart glasses. This allows the service provider to provide appropriate advice according to the user's emotions, enabling conversations that enhance the user's sense of security.

[0199] For example, if a user visits with concerns about medication side effects, sentiment analysis software detects the anxiety, and generative artificial intelligence generates reassuring information. The display system allows staff to explain to the user in an appropriate tone, "This medication has few side effects and is safe." An example of a prompt might be: "The customer is concerned about medication side effects. Please generate reassuring and gentle persuasive advice."

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The server receives user health information and voice / visual data through the terminal. This input data includes the user's self-reported health status, medication history, voice tone, and facial expressions. The server stores this data digitally and integrates it into the initial database.

[0203] Step 2:

[0204] The server passes the received audio and visual data to an emotion analysis system to analyze the user's emotional state. The input data is processed in real time by emotion recognition software to identify the user's possible emotions (anxiety, relief, stress). This result is temporarily stored as intermediate data.

[0205] Step 3:

[0206] The server analyzes health information and generates personalized medication prescription advice using a generative AI model. This input includes the user's health status data, and the generative AI model, referencing a database, generates information on the most suitable medications for the user. The output advice is linked to the user's associated emotional data.

[0207] Step 4:

[0208] The terminal receives generated advice and sentiment analysis results sent from the server. This input information is transmitted to the display device and visually shown to the service provider in real time. The displayed information helps the service provider provide appropriate explanations to the user.

[0209] Step 5:

[0210] Customer service staff provide personalized medication advice to users based on information obtained from display devices. This approach takes into account the user's emotional state, and the information displayed is adjusted to provide a sense of reassurance.

[0211] Step 6:

[0212] Users input feedback into their devices, and the server receives this information. This feedback includes changes in the user's emotions and new health information. The server updates its database and uses this information for future analysis and training of generative AI models.

[0213] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0214] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0215] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0219] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0220] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0221] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0222] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0223] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0224] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0225] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0226] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0227] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0228] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0229] This invention relates to a medication advice system using an AI robot installed in the home. This system supports appropriate medication management by providing personalized medication advice based on the user's health information.

[0230] 1. User interface operation:

[0231] Users can access the system through their devices and input their health information, medical history, and allergy information. This includes voice input and text input using a touch panel.

[0232] 2. Analysis of health data:

[0233] The user's health information entered from the terminal is collected by the server. The server analyzes this information and uses generative artificial intelligence to generate optimal medication prescription advice for the user. This advice includes appropriate dosage, warnings about side effects, and risks of drug interactions.

[0234] 3. Providing advice:

[0235] The generated advice is sent from the server to the terminal. The terminal displays the advice to the user and provides audio explanations as needed. This allows the user to receive clear instructions on their own device.

[0236] 4. Managing medication schedules:

[0237] Based on a medication plan generated by the server, the device manages the user's medication schedule. As the time to take medication approaches, the device prompts the user to take their medication with voice and visual alerts.

[0238] 5. Feedback and data updates:

[0239] After taking medication, users input changes in their physical condition and new health information into their device and send it to the server. The server updates the data based on this feedback and incorporates it into future advice. This cycle ensures that the system always provides the latest and most personalized support.

[0240] Specific example:

[0241] For example, if a user suffering from high blood pressure uses the system, they input their medical history and current medications. The server analyzes this information and suggests new medications and dosages. Furthermore, based on the advice, it creates a medication schedule tailored to the user's schedule, such as before breakfast or before bed, allowing them to easily integrate their medication into their daily life.

[0242] This system supports users in taking medications tailored to their individual health conditions, thereby contributing to their overall health maintenance.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] The user enters their health information into the terminal. Input methods include voice input or text input via a touchscreen. The terminal converts this information into text data and then into a format suitable for transmission to the server.

[0246] Step 2:

[0247] The server receives the user's health information sent from the terminal. The server stores this information in a database and prepares it for analysis. Simultaneously, it also performs encryption to protect privacy.

[0248] Step 3:

[0249] The server uses stored information and generative artificial intelligence to generate prescription advice tailored to the user. This includes selecting medications that take into account the user's medical history and allergy information, as well as warnings about side effects.

[0250] Step 4:

[0251] The server sends the generated advice to the terminal. The terminal prepares to present the received information to the user. The information can be displayed not only on the screen but also through voice guidance.

[0252] Step 5:

[0253] The device creates a medication schedule based on the generated advice. It sets medication times that fit the user's lifestyle and provides a function to notify the user of the schedule.

[0254] Step 6:

[0255] Users can take medication according to the advice given and report any changes in their physical condition or side effects they experience after taking it to their device. This provides feedback information that helps improve the quality of future advice.

[0256] Step 7:

[0257] The terminal processes user feedback information and sends it to the server. The server reflects this information in its database and updates the data for use in generating advice the next time. This cycle ensures that the system always reflects the latest information and can provide highly accurate advice.

[0258] (Example 1)

[0259] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0260] In modern society, personalized health management is in demand, but traditional methods have the challenge of not being able to properly prescribe medications and manage schedules according to the individual user's information. This problem is particularly serious for people who use a wide variety of medications, as incorrect use can lead to health problems.

[0261] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0262] In this invention, the server includes information acquisition means for inputting the user's physical condition information, instruction generation means for analyzing the input physical condition information and generating personalized drug prescription instructions using generative intelligence, and instruction presentation means for presenting the generated instructions to the user. This makes it possible to provide the user with the most suitable drug prescription and appropriate medication management.

[0263] "Information acquisition means" refers to a device or method for inputting information about a user's physical condition.

[0264] "Instruction generation means" refers to a device or method for analyzing input physical condition information and generating personalized drug prescription instructions using generative intelligence.

[0265] "Instruction presentation means" refers to a device or method for presenting prescription instructions for a generated drug to the user.

[0266] "Schedule management means" refers to a device or method for managing a user's medication schedule based on the prescription instructions for the generated drugs and for notifying the user of the schedule.

[0267] "Information update means" refers to a device or method for collecting additional information and opinions from users and updating the memory area.

[0268] "Generative intelligence" is an artificial intelligence technology that has the ability to generate optimal drug prescription instructions based on input data.

[0269] This invention relates to a drug administration instruction system using an artificial intelligence-equipped device installed in the home. This system provides personalized drug instructions based on the user's physical condition information, supporting appropriate medication management.

[0270] Users can access the system through terminals equipped with voice input or touch panels. The terminals collect information on the user's physical condition, medical history, and allergies, and send this information to the server. The server analyzes this data and uses a generative AI model to generate medication recommendations optimized for each individual user.

[0271] Instructions generated by the server are sent to the terminal and provided to the user. The terminal visually displays the generated instructions on the screen, and can also provide instructions by voice if the user has difficulty accessing visual information. This allows users to receive personalized instructions based on their health condition from the comfort of their homes.

[0272] As a concrete example, a user suffering from high blood pressure enters their blood pressure readings and information about medications they are currently taking into the terminal. Based on this information, the server generates specific instructions such as "Take medication X twice a day, in the morning and at night," and presents them to the user. The terminal then issues an alert at the specified time to prompt the user to take their medication.

[0273] As an example of a prompt, you could use a text-based instruction such as, "We want to develop an AI system that takes user health information and generates personalized medication recommendations. What kind of prompts would be effective?"

[0274] In this way, the system of the present invention can efficiently perform individual health management and contribute to maintaining the health of users.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The user inputs their physical condition information, medical history, and allergy information using a terminal. The input methods include text input through a touch panel and voice input through voice recognition. This input data is important as basic information for understanding the user's health condition. The terminal sequentially transmits this information to the server.

[0278] Step 2:

[0279] The server receives the physical condition information sent from the terminal, collates it with the database, and performs analysis. Using a generated AI model, it analyzes the user's information in detail and generates individualized prescription instructions for medications. This generation process takes into account the appropriate type of medication, dosage, and side effect information based on the input data.

[0280] Step 3:

[0281] The generated medication instructions are sent from the server to the terminal. The terminal visually displays the instructions on the screen and, if necessary, also presents them audibly. This allows the user to confirm the medication information suitable for themselves in an intuitively understandable format. As a specific example, instructions such as "Please take 1 tablet of Medicine X after breakfast and 1 tablet after dinner" are output.

[0282] Step 4:

[0283] The terminal automatically creates and manages the user's medication schedule based on the instructions sent from the server. When the designated medication time approaches, it notifies the user through an alert sound or a screen notification. This is a function to prevent the user from forgetting to take their medicine and provides support by audibly notifying "It's time to take your medicine".

[0284] Step 5:

[0285] When the user takes medicine, they input the changes in their physical condition and newly noticed points as feedback into the terminal. This feedback information is sent to the server as data to be utilized in generating the next advice. The server updates the database based on the received feedback and reflects it in the generative AI model to enable more appropriate advice.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] When managing the user's medicine intake, there is an issue that it is difficult to prevent forgetting to take the medicine or making mistakes in the dosage while out. Especially in situations where one is moving or in a public place and has to be cautious of others, it is easy to forget to take the medicine appropriately. Also, it is required to grasp the user's health information in real time and update the medication plan each time. It is necessary to solve these problems and support the proper management and intake of medicine.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0290] In this invention, the server includes a data input means for the user to input their health information, an advice generation means for analyzing the input health information and generating personalized medicine prescription advice by a generative artificial intelligence, a data synchronization means for synchronizing health data on the cloud, and an information providing means for providing the generated advice to the user. Thereby, the user can receive an appropriate medicine intake plan even when away from home and prevent mistakes in taking medicine.

[0291] The "data input means" is a device or method for the user to input their health information into the system.

[0292] An "advice generation means" is a device or method that uses generative artificial intelligence to create personalized drug prescription advice based on input health information.

[0293] "Data synchronization means" refers to devices or methods for updating and aligning health information on the cloud.

[0294] "Information provision means" refers to devices or methods that provide generated advice to users in an easily understandable format.

[0295] A "notification management system" is a device or method that manages a user's medication plan based on generated advice and provides notifications at the appropriate time.

[0296] "Information update means" refers to devices and methods for collecting additional information and feedback from users and keeping the information infrastructure constantly up-to-date.

[0297] This invention is a system for users to manage their health information and receive personalized medication advice. The server acquires health information from the user through a data input means. This health information is entered via the user's smartphone, tablet, or other device. For voice input, the system utilizes a "voice recognition API" on smart devices to convert the entered information into text in real time.

[0298] Next, the server uses an advice generation mechanism to analyze the collected health information and generates optimal medication prescription advice for the user using a generation AI model. This generation utilizes a "processing service" in a cloud computing environment, leveraging high-performance computing capabilities to provide fast and accurate prescription advice.

[0299] Furthermore, the generated advice is synchronized on the cloud and kept up-to-date at all times through data synchronization mechanisms. This information is sent to the user's device and provided to the user via audio and visual means through information delivery mechanisms. This ensures that users can always obtain accurate medication information no matter where they are.

[0300] For example, if a user is out and about and it's time to take their medication, the device will send a reminder via the notification management system. This allows the user to take their medication properly by following voice instructions, even in public places, without worrying about being seen by others.

[0301] Furthermore, post-medication feedback is sent to the cloud via an information update mechanism, keeping the database constantly up-to-date. This data is used by the server to generate advice for the next dose. An example of a prompt would be: "The user's latest blood glucose level is 150 mg / dL. Please generate advice for the next dose, taking into account meal timing and medication history." This prompt is used as input to an AI model, which generates a medication plan tailored to the individual's situation.

[0302] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0303] Step 1:

[0304] Users enter health information through their device. This information includes age, gender, medical history, allergy information, and current medications. This input is performed using a "voice recognition API" or touch panel and sent to the server.

[0305] Step 2:

[0306] The server receives the health information obtained via the data input means and generates a prompt for the generative AI model based on that information. This prompt contains user-specific data necessary to provide advice on medications suitable for the user. After this prompt is generated, it is sent to the generative AI model.

[0307] Step 3:

[0308] The generative AI model generates advice on the optimal prescription of medications for the user based on the received prompt. Through this process, advice is generated that includes specific dosages and precautions tailored to the user's individual health condition and lifestyle. The generated advice is returned to the server.

[0309] Step 4:

[0310] The server synchronizes the newly generated advice onto the cloud. At this time, using the data synchronization means, all advice data is reconciled to be up-to-date in preparation for future advice generation and user notifications.

[0311] Step 5:

[0312] The advice sent from the server is presented to the user via the information providing means of the terminal. On the terminal, the voice output function and the display function are used to enable the user to visually and audibly confirm the advice. This step supports how the user can actually use that advice.

[0313] Step 6:

[0314] The user takes the medication based on the advice and then enters the subsequent feedback into the terminal. This feedback includes the actual time of taking the medication and changes in physical condition. This data is sent to the server and the information in the database is updated by the information updating means.

[0315] Step 7:

[0316] The server uses the updated data to prepare for generating the next set of advice. This allows for the optimization of medication management while consistently providing users with the most appropriate medication advice.

[0317] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0318] This invention relates to a home-use AI robot pharmacist system that includes an emotion engine. It not only personalizes medication prescription advice based on the user's health information but also considers the user's emotional state. This system optimizes the user's medication experience by comprehensively combining information input, emotion recognition, advice generation, and advice delivery functions.

[0319] 1. User Interface and Emotion Recognition:

[0320] When users input health information through their devices, their voice or visual responses are recorded. The device analyzes these responses and recognizes the user's emotions through an emotion engine. This allows the system to understand the user's psychological state (e.g., anxiety, reassurance, stress).

[0321] 2. Analysis of health data and emotional data:

[0322] The server receives health information and emotional data transmitted from the terminal. An emotion engine analyzes the user's emotions and combines them with health information for a comprehensive evaluation. Generative artificial intelligence then generates personalized prescription advice based on this data.

[0323] 3. Providing advice that is tailored to the individual's emotions:

[0324] The server sends the generated advice to the terminal, and the terminal provides the advice in a tone and content that suits the user's emotional state. For example, a user who is feeling anxious will receive a calm and reassuring explanation.

[0325] 4. Advice and Schedule Management:

[0326] The device manages the medication schedule while considering the user's emotional state and provides emotionally sensitive notifications. For example, reminders can be customized in the user's preferred voice or visual style.

[0327] 5. Feedback and data updates:

[0328] Users input feedback into their device about changes in their feelings and emotions after taking medication. The server receives the new emotional data, updates its database, and uses it for the next analysis.

[0329] Specific example:

[0330] If a user is using the system while feeling anxious about medication side effects, the terminal detects this anxiety from the user's tone of voice and words. Based on this information, the server generates advice to reduce the risk of side effects in order to provide reassurance. The terminal then delivers this advice in a quiet and calming tone to alleviate the user's anxiety.

[0331] Thus, the present invention aims to enable flexible responses that take into account the user's psychological state and to provide a better experience regarding medication use.

[0332] The following describes the processing flow.

[0333] Step 1:

[0334] Users input health information into the device via voice or text. During this process, the device also records the user's voice tone and word choices, collecting data for analysis by an emotion engine.

[0335] Step 2:

[0336] The device sends the user's health information and emotional data to the server. The emotion engine analyzes voice tone and facial expression data to identify the user's emotional state. For example, if the user is feeling anxious or stressed, it extracts that information.

[0337] Step 3:

[0338] The server integrates and analyzes received health information and emotional data. Using generative artificial intelligence, it generates personalized medication prescription advice that takes into account the user's emotional state. This advice includes wording tailored to the user's psychological condition.

[0339] Step 4:

[0340] The server sends the generated advice to the device. The device displays or provides the advice in a tone and language that suits the user's emotions. For users who are feeling anxious, the advice is explained in a relaxed tone, demonstrating an emotionally sensitive approach.

[0341] Step 5:

[0342] Users take medication based on the advice provided. After taking the medication, they input feedback into their device and report their emotional state after taking it. This provides data to improve future advice.

[0343] Step 6:

[0344] The device organizes user feedback and sends it to the server. The server records this information in a database, uses an emotion engine to update the data, and uses it to generate advice for the next time. This process ensures that the system always provides the best possible support to the user using the most up-to-date information.

[0345] (Example 2)

[0346] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0347] In modern medication management systems, providing a personalized medication experience that takes into account the user's emotional state is challenging. In particular, prescription advice and medication schedules often fail to reflect the user's psychological state, resulting in insufficient reduction of anxiety and stress.

[0348] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0349] In this invention, the server includes data acquisition means for receiving information about the user's health, emotion analysis means for analyzing the user's voice and images to recognize their emotional state, and advice generation means for integrating health information and emotion data and generating personalized prescription advice using a generation AI model. This enables the provision of flexible and personalized advice according to the user's emotional state and the management of medication schedules.

[0350] "Data acquisition means" refers to devices or methods that have the function of collecting health-related information from users.

[0351] "Emotional analysis means" refers to technologies and devices for identifying a user's emotional state from their voice or images.

[0352] A "generative AI model" is a machine learning model that generates personalized results based on specific input data.

[0353] An "advice generation means" is a device or method that has the function of creating prescription advice optimized for the user using acquired health information and emotional data.

[0354] "An advice delivery method" refers to a device or method for presenting generated advice in a way that is appropriate to the user's emotional state.

[0355] A "schedule management tool" refers to a device or method that has the function of creating a medication plan for a user and notifying them in an emotionally sensitive manner.

[0356] "Data update methods" refer to technologies and devices that update databases based on user feedback to improve analysis accuracy.

[0357] This invention is a system that provides personalized prescription advice using the user's health information and emotional state. The system primarily consists of terminals and a server, and by integrating these functions, it provides the user with an optimal medication management experience.

[0358] Specifically, the device provides an interface for users to input health-related data. This includes interfaces that utilize voice and visuals. The data entered by the user is processed by the device's emotion analysis engine. In this process, voice and facial expression data are analyzed to recognize the user's emotional state.

[0359] The analyzed data is sent to a server. The server uses an emotion analysis engine and a generative AI model to integrate health data and emotion data and generate personalized prescription advice. The generated advice is sent to the device in a format that is sensitive to the user's emotions.

[0360] The device provides the user with received advice. The advice is presented in an appropriate tone using voice and visuals, designed to reduce the user's anxiety and stress. The device also manages the user's medication schedule and provides notifications that are sensitive to the user's emotional state, ensuring medication adherence.

[0361] This allows the system to provide personalized advice and plan management based on the user's emotions and health status, thereby supporting the user's well-being.

[0362] As a concrete example, if a user is feeling anxious about taking medication, the device will sense this emotion, and the server will generate specific advice to alleviate it. For example, it might offer a suggestion in a calm tone, such as, "It's a good idea to drink plenty of water when taking your medication." An example of a prompt might be, "Please evaluate the user's current emotional state based on their voice data and text analysis. If anxiety is detected, please suggest specific medication advice to alleviate it."

[0363] Thus, this invention aims to realize flexible medication management that comprehensively considers the user's psychological state and health condition.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] Users input health data using a terminal. This data includes current medication information, changes in physical condition, and medical history. This health data is used by the system as basic information to understand the user's condition.

[0367] Step 2:

[0368] The device collects voice and visual information along with the user's health data. It uses the built-in microphone and camera to record the user's facial expressions and voice tone. Using this data, the emotion analysis engine identifies the user's emotional state (e.g., anxiety, reassurance).

[0369] Step 3:

[0370] The device transmits collected health and emotional data to the server. This transmitted data is integrated on the server side and becomes input information to understand the user's overall state.

[0371] Step 4:

[0372] The server analyzes the received health information and emotional data. Based on the emotional analysis results, a generative AI model generates prescription advice tailored to the user. For example, if the user is experiencing anxiety, advice is generated that takes that emotion into account and promotes a sense of security.

[0373] Step 5:

[0374] The server sends the generated advice to the terminal. The terminal then displays the received advice to the user. In this process, speech synthesis technology and a display are used to provide the advice in a tone that matches the user's emotional state.

[0375] Step 6:

[0376] The device manages the user's medication schedule based on generated advice. A reminder function is used to notify the user to take their medication at the appropriate time. Notifications are customized according to the user's preferences.

[0377] Step 7:

[0378] Users input feedback into the device about changes in their physical condition and emotions after taking medication. This feedback serves as important data for the system to improve the accuracy of future prescription advice.

[0379] Step 8:

[0380] The server receives feedback from users and updates its sentiment database. This updated data is then used in subsequent analyses and advice generation. Through this process, the system continuously evolves, enabling it to provide more appropriate advice.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0383] In modern society, users often feel anxious about choosing and using medications at pharmacies. Furthermore, accurately understanding a user's psychological state and providing personalized, effective medication advice is difficult in face-to-face interactions. In this situation, there is a need for a system that can respond appropriately to users' emotions.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0385] In this invention, the server includes an information input means for inputting the user's health information, an advice generation means for analyzing the input health information and generating personalized drug prescription advice using generative artificial intelligence, an advice provision means for providing the generated advice to the user, an emotion analysis means for analyzing the user's emotional information and providing information in an appropriate tone according to the emotion, and a display means for maintaining the user's emotional state in a face-to-face environment and providing information to the service provider. This enables accurate understanding of the user's psychological state during face-to-face service at a pharmacy and allows for personalized and effective advice.

[0386] "Information input means" refers to a device or method for acquiring health-related information collected from users as digital data.

[0387] "Advice generation means" refers to a device or method for generating individually optimized drug prescription advice using generative artificial intelligence based on the user's health information.

[0388] "Advice provision means" refers to a device or method for providing generated advice to a user in an appropriate format.

[0389] "Emotion analysis means" refers to a device or method that has the function of identifying and analyzing emotions from a user's voice, facial expressions, etc.

[0390] "Display means" refers to a device or part of a device that visually represents analyzed information or generated advice and provides it to the customer.

[0391] A system for carrying out this invention includes information input means, advice generation means, advice provision means, emotion analysis means, and display means.

[0392] The server collects health information provided by the user through an information input method. This information input method utilizes hardware devices such as smart glasses or voice input devices to acquire the user's voice and image data in digital format. The collected data is stored on a cloud server and used for analysis.

[0393] Next, the server uses generative artificial intelligence to generate personalized medication prescription advice through an advice generation mechanism. The generative AI cross-references various health databases to generate advice best suited to the user's health condition. This enables the recommendation of medications tailored to the user's specific circumstances.

[0394] Furthermore, emotion analysis techniques are used to analyze the user's emotional state from their voice and facial expression data. Specifically, existing emotion recognition software (e.g., Affectiva or IBM Watson) can be used to understand the user's psychological state in real time.

[0395] The generated advice and emotion analysis results are provided to the service provider through display devices such as smart glasses. This allows the service provider to provide appropriate advice according to the user's emotions, enabling conversations that enhance the user's sense of security.

[0396] For example, if a user visits with concerns about medication side effects, sentiment analysis software detects the anxiety, and generative artificial intelligence generates reassuring information. The display system allows staff to explain to the user in an appropriate tone, "This medication has few side effects and is safe." An example of a prompt might be: "The customer is concerned about medication side effects. Please generate reassuring and gentle persuasive advice."

[0397] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0398] Step 1:

[0399] The server receives user health information and voice / visual data through the terminal. This input data includes the user's self-reported health status, medication history, voice tone, and facial expressions. The server stores this data digitally and integrates it into the initial database.

[0400] Step 2:

[0401] The server passes the received audio and visual data to an emotion analysis system to analyze the user's emotional state. The input data is processed in real time by emotion recognition software to identify the user's possible emotions (anxiety, relief, stress). This result is temporarily stored as intermediate data.

[0402] Step 3:

[0403] The server analyzes health information and generates personalized medication prescription advice using a generative AI model. This input includes the user's health status data, and the generative AI model, referencing a database, generates information on the most suitable medications for the user. The output advice is linked to the user's associated emotional data.

[0404] Step 4:

[0405] The terminal receives generated advice and sentiment analysis results sent from the server. This input information is transmitted to the display device and visually shown to the service provider in real time. The displayed information helps the service provider provide appropriate explanations to the user.

[0406] Step 5:

[0407] Customer service staff provide personalized medication advice to users based on information obtained from display devices. This approach takes into account the user's emotional state, and the information displayed is adjusted to provide a sense of reassurance.

[0408] Step 6:

[0409] Users input feedback into their devices, and the server receives this information. This feedback includes changes in the user's emotions and new health information. The server updates its database and uses this information for future analysis and training of generative AI models.

[0410] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0411] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0412] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0413] [Third Embodiment]

[0414] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0415] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0416] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0417] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0418] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0419] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0420] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0421] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0422] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0423] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0424] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0425] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0426] This invention relates to a medication advice system using an AI robot installed in the home. This system supports appropriate medication management by providing personalized medication advice based on the user's health information.

[0427] 1. User interface operation:

[0428] Users can access the system through their devices and input their health information, medical history, and allergy information. This includes voice input and text input using a touch panel.

[0429] 2. Analysis of health data:

[0430] The user's health information entered from the terminal is collected by the server. The server analyzes this information and uses generative artificial intelligence to generate optimal medication prescription advice for the user. This advice includes appropriate dosage, warnings about side effects, and risks of drug interactions.

[0431] 3. Providing advice:

[0432] The generated advice is sent from the server to the terminal. The terminal displays the advice to the user and provides audio explanations as needed. This allows the user to receive clear instructions on their own device.

[0433] 4. Managing medication schedules:

[0434] Based on a medication plan generated by the server, the device manages the user's medication schedule. As the time to take medication approaches, the device prompts the user to take their medication with voice and visual alerts.

[0435] 5. Feedback and data updates:

[0436] After taking medication, users input changes in their physical condition and new health information into their device and send it to the server. The server updates the data based on this feedback and incorporates it into future advice. This cycle ensures that the system always provides the latest and most personalized support.

[0437] Specific example:

[0438] For example, if a user suffering from high blood pressure uses the system, they input their medical history and current medications. The server analyzes this information and suggests new medications and dosages. Furthermore, based on the advice, it creates a medication schedule tailored to the user's schedule, such as before breakfast or before bed, allowing them to easily integrate their medication into their daily life.

[0439] This system supports users in taking medications tailored to their individual health conditions, thereby contributing to their overall health maintenance.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The user enters their health information into the terminal. Input methods include voice input or text input via a touchscreen. The terminal converts this information into text data and then into a format suitable for transmission to the server.

[0443] Step 2:

[0444] The server receives the user's health information sent from the terminal. The server stores this information in a database and prepares it for analysis. Simultaneously, it also performs encryption to protect privacy.

[0445] Step 3:

[0446] The server uses stored information and generative artificial intelligence to generate prescription advice tailored to the user. This includes selecting medications that take into account the user's medical history and allergy information, as well as warnings about side effects.

[0447] Step 4:

[0448] The server sends the generated advice to the terminal. The terminal prepares to present the received information to the user. The information can be displayed not only on the screen but also through voice guidance.

[0449] Step 5:

[0450] The device creates a medication schedule based on the generated advice. It sets medication times that fit the user's lifestyle and provides a function to notify the user of the schedule.

[0451] Step 6:

[0452] Users can take medication according to the advice given and report any changes in their physical condition or side effects they experience after taking it to their device. This provides feedback information that helps improve the quality of future advice.

[0453] Step 7:

[0454] The terminal processes user feedback information and sends it to the server. The server reflects this information in its database and updates the data for use in generating advice the next time. This cycle ensures that the system always reflects the latest information and can provide highly accurate advice.

[0455] (Example 1)

[0456] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0457] In modern society, personalized health management is in demand, but traditional methods have the challenge of not being able to properly prescribe medications and manage schedules according to the individual user's information. This problem is particularly serious for people who use a wide variety of medications, as incorrect use can lead to health problems.

[0458] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0459] In this invention, the server includes information acquisition means for inputting the user's physical condition information, instruction generation means for analyzing the input physical condition information and generating personalized drug prescription instructions using generative intelligence, and instruction presentation means for presenting the generated instructions to the user. This makes it possible to provide the user with the most suitable drug prescription and appropriate medication management.

[0460] "Information acquisition means" refers to a device or method for inputting information about a user's physical condition.

[0461] "Instruction generation means" refers to a device or method for analyzing input physical condition information and generating personalized drug prescription instructions using generative intelligence.

[0462] "Instruction presentation means" refers to a device or method for presenting prescription instructions for a generated drug to the user.

[0463] "Schedule management means" refers to a device or method for managing a user's medication schedule based on the prescription instructions for the generated drugs and for notifying the user of the schedule.

[0464] "Information update means" refers to a device or method for collecting additional information and opinions from users and updating the memory area.

[0465] "Generative intelligence" is an artificial intelligence technology that has the ability to generate optimal drug prescription instructions based on input data.

[0466] This invention relates to a drug administration instruction system using an artificial intelligence-equipped device installed in the home. This system provides personalized drug instructions based on the user's physical condition information, supporting appropriate medication management.

[0467] Users can access the system through terminals equipped with voice input or touch panels. The terminals collect information on the user's physical condition, medical history, and allergies, and send this information to the server. The server analyzes this data and uses a generative AI model to generate medication recommendations optimized for each individual user.

[0468] Instructions generated by the server are sent to the terminal and provided to the user. The terminal visually displays the generated instructions on the screen, and can also provide instructions by voice if the user has difficulty accessing visual information. This allows users to receive personalized instructions based on their health condition from the comfort of their homes.

[0469] As a concrete example, a user suffering from high blood pressure enters their blood pressure readings and information about medications they are currently taking into the terminal. Based on this information, the server generates specific instructions such as "Take medication X twice a day, in the morning and at night," and presents them to the user. The terminal then issues an alert at the specified time to prompt the user to take their medication.

[0470] As an example of a prompt, you could use a text-based instruction such as, "We want to develop an AI system that takes user health information and generates personalized medication recommendations. What kind of prompts would be effective?"

[0471] In this way, the system of the present invention can efficiently perform individual health management and contribute to maintaining the health of users.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] Users input their physical condition, medical history, and allergy information using a terminal. Input methods include text input via a touch panel and voice input via voice recognition. This input data is important as basic information for understanding the user's health status. The terminal transmits this information to the server sequentially.

[0475] Step 2:

[0476] The server receives physical condition information sent from the terminal, compares it with the database, and performs analysis. Using a generative AI model, it analyzes the user's information in detail and generates personalized medication prescriptions. This generation process takes into account the input data, considering appropriate medication types, dosages, and side effect information.

[0477] Step 3:

[0478] The generated medication instructions are sent from the server to the terminal. The terminal displays the instructions visually on the screen and, if necessary, also provides them audibly. This allows the user to check medication information tailored to their needs in an intuitively understandable format. For example, instructions such as "Take one tablet of medication X after breakfast and one tablet after dinner" are output.

[0479] Step 4:

[0480] The device automatically creates and manages the user's medication schedule based on instructions sent from the server. As the designated medication time approaches, it notifies the user with an alert sound and screen notification. This feature helps prevent users from forgetting to take their medication, and provides support by notifying them with a voice message saying, "It's time to take your medicine."

[0481] Step 5:

[0482] Users input feedback into their device regarding changes in their physical condition after taking medication and any new observations they may have. This feedback information is sent to the server as data to be used in generating advice for the next time. The server updates its database based on the received feedback and incorporates it into the AI ​​model to provide more appropriate advice.

[0483] (Application Example 1)

[0484] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0485] Managing users' medication adherence presents challenges, particularly in preventing missed doses or incorrect dosages while out and about. This is especially true in public places or during travel, where users are more likely to forget to take their medication properly. Furthermore, real-time monitoring of users' health information and updating medication plans accordingly are essential. Addressing these challenges and supporting proper medication management and adherence is crucial.

[0486] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0487] In this invention, the server includes data input means for inputting the user's health information, advice generation means for analyzing the input health information and generating personalized medication prescription advice using generative artificial intelligence, data synchronization means for synchronizing health data to the cloud, and information provision means for providing the generated advice to the user. This allows the user to receive an appropriate medication schedule even when away from home, preventing medication errors.

[0488] "Data input means" refers to devices or methods for users to input their health information into a system.

[0489] An "advice generation means" is a device or method that uses generative artificial intelligence to create personalized drug prescription advice based on input health information.

[0490] "Data synchronization means" refers to devices or methods for updating and aligning health information on the cloud.

[0491] "Information provision means" refers to devices or methods that provide generated advice to users in an easily understandable format.

[0492] A "notification management system" is a device or method that manages a user's medication plan based on generated advice and provides notifications at the appropriate time.

[0493] "Information update means" refers to devices and methods for collecting additional information and feedback from users and keeping the information infrastructure constantly up-to-date.

[0494] This invention is a system for users to manage their health information and receive personalized medication advice. The server acquires health information from the user through a data input means. This health information is entered via the user's smartphone, tablet, or other device. For voice input, the system utilizes a "voice recognition API" on smart devices to convert the entered information into text in real time.

[0495] Next, the server uses an advice generation mechanism to analyze the collected health information and generates optimal medication prescription advice for the user using a generation AI model. This generation utilizes a "processing service" in a cloud computing environment, leveraging high-performance computing capabilities to provide fast and accurate prescription advice.

[0496] Furthermore, the generated advice is synchronized on the cloud and kept up-to-date at all times through data synchronization mechanisms. This information is sent to the user's device and provided to the user via audio and visual means through information delivery mechanisms. This ensures that users can always obtain accurate medication information no matter where they are.

[0497] For example, if a user is out and about and it's time to take their medication, the device will send a reminder via the notification management system. This allows the user to take their medication properly by following voice instructions, even in public places, without worrying about being seen by others.

[0498] Furthermore, post-medication feedback is sent to the cloud via an information update mechanism, keeping the database constantly up-to-date. This data is used by the server to generate advice for the next dose. An example of a prompt would be: "The user's latest blood glucose level is 150 mg / dL. Please generate advice for the next dose, taking into account meal timing and medication history." This prompt is used as input to an AI model, which generates a medication plan tailored to the individual's situation.

[0499] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0500] Step 1:

[0501] Users enter health information through their device. This information includes age, gender, medical history, allergy information, and current medications. This input is performed using a "voice recognition API" or touch panel and sent to the server.

[0502] Step 2:

[0503] The server receives health information acquired through data input and generates prompts for the generating AI model based on that information. These prompts include user-specific data necessary to provide appropriate medication advice to the user. After these prompts are generated, they are sent to the generating AI model.

[0504] Step 3:

[0505] The generation AI model generates optimal medication prescription advice for the user based on the received prompts. This process produces advice that includes specific dosages and precautions tailored to the user's individual health condition and lifestyle. The generated advice is returned to the server.

[0506] Step 4:

[0507] The server synchronizes newly generated advice to the cloud. During this process, data synchronization is used to ensure all advice data is up-to-date, preparing for future advice generation and user notifications.

[0508] Step 5:

[0509] Advice sent from the server is presented to the user via the terminal's information delivery system. The terminal uses audio output and display functions to allow the user to visually and audibly confirm the advice. This step supports how the user actually uses that advice.

[0510] Step 6:

[0511] Users take their medication based on the advice they receive and then input feedback into their device. This feedback includes the actual time of administration and any changes in their physical condition. This data is sent to a server, and the database is updated using an information update mechanism.

[0512] Step 7:

[0513] The server uses the updated data to prepare for generating the next set of advice. This allows for the optimization of medication management while consistently providing users with the most appropriate medication advice.

[0514] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0515] This invention relates to a home-use AI robot pharmacist system that includes an emotion engine. It not only personalizes medication prescription advice based on the user's health information but also considers the user's emotional state. This system optimizes the user's medication experience by comprehensively combining information input, emotion recognition, advice generation, and advice delivery functions.

[0516] 1. User Interface and Emotion Recognition:

[0517] When users input health information through their devices, their voice or visual responses are recorded. The device analyzes these responses and recognizes the user's emotions through an emotion engine. This allows the system to understand the user's psychological state (e.g., anxiety, reassurance, stress).

[0518] 2. Analysis of health data and emotional data:

[0519] The server receives health information and emotional data transmitted from the terminal. An emotion engine analyzes the user's emotions and combines them with health information for a comprehensive evaluation. Generative artificial intelligence then generates personalized prescription advice based on this data.

[0520] 3. Providing advice that is tailored to the individual's emotions:

[0521] The server sends the generated advice to the terminal, and the terminal provides the advice in a tone and content that suits the user's emotional state. For example, a user who is feeling anxious will receive a calm and reassuring explanation.

[0522] 4. Advice and Schedule Management:

[0523] The device manages the medication schedule while considering the user's emotional state and provides emotionally sensitive notifications. For example, reminders can be customized in the user's preferred voice or visual style.

[0524] 5. Feedback and data updates:

[0525] Users input feedback into their device about changes in their feelings and emotions after taking medication. The server receives the new emotional data, updates its database, and uses it for the next analysis.

[0526] Specific example:

[0527] If a user is using the system while feeling anxious about medication side effects, the terminal detects this anxiety from the user's tone of voice and words. Based on this information, the server generates advice to reduce the risk of side effects in order to provide reassurance. The terminal then delivers this advice in a quiet and calming tone to alleviate the user's anxiety.

[0528] Thus, the present invention aims to enable flexible responses that take into account the user's psychological state and to provide a better experience regarding medication use.

[0529] The following describes the processing flow.

[0530] Step 1:

[0531] Users input health information into the device via voice or text. During this process, the device also records the user's voice tone and word choices, collecting data for analysis by an emotion engine.

[0532] Step 2:

[0533] The device sends the user's health information and emotional data to the server. The emotion engine analyzes voice tone and facial expression data to identify the user's emotional state. For example, if the user is feeling anxious or stressed, it extracts that information.

[0534] Step 3:

[0535] The server integrates and analyzes received health information and emotional data. Using generative artificial intelligence, it generates personalized medication prescription advice that takes into account the user's emotional state. This advice includes wording tailored to the user's psychological condition.

[0536] Step 4:

[0537] The server sends the generated advice to the device. The device displays or provides the advice in a tone and language that suits the user's emotions. For users who are feeling anxious, the advice is explained in a relaxed tone, demonstrating an emotionally sensitive approach.

[0538] Step 5:

[0539] Users take medication based on the advice provided. After taking the medication, they input feedback into their device and report their emotional state after taking it. This provides data to improve future advice.

[0540] Step 6:

[0541] The device organizes user feedback and sends it to the server. The server records this information in a database, uses an emotion engine to update the data, and uses it to generate advice for the next time. This process ensures that the system always provides the best possible support to the user using the most up-to-date information.

[0542] (Example 2)

[0543] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0544] In modern medication management systems, providing a personalized medication experience that takes into account the user's emotional state is challenging. In particular, prescription advice and medication schedules often fail to reflect the user's psychological state, resulting in insufficient reduction of anxiety and stress.

[0545] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0546] In this invention, the server includes data acquisition means for receiving information about the user's health, emotion analysis means for analyzing the user's voice and images to recognize their emotional state, and advice generation means for integrating health information and emotion data and generating personalized prescription advice using a generation AI model. This enables the provision of flexible and personalized advice according to the user's emotional state and the management of medication schedules.

[0547] "Data acquisition means" refers to devices or methods that have the function of collecting health-related information from users.

[0548] "Emotional analysis means" refers to technologies and devices for identifying a user's emotional state from their voice or images.

[0549] A "generative AI model" is a machine learning model that generates personalized results based on specific input data.

[0550] An "advice generation means" is a device or method that has the function of creating prescription advice optimized for the user using acquired health information and emotional data.

[0551] "An advice delivery method" refers to a device or method for presenting generated advice in a way that is appropriate to the user's emotional state.

[0552] A "schedule management tool" refers to a device or method that has the function of creating a medication plan for a user and notifying them in an emotionally sensitive manner.

[0553] "Data update methods" refer to technologies and devices that update databases based on user feedback to improve analysis accuracy.

[0554] This invention is a system that provides personalized prescription advice using the user's health information and emotional state. The system primarily consists of terminals and a server, and by integrating these functions, it provides the user with an optimal medication management experience.

[0555] Specifically, the device provides an interface for users to input health-related data. This includes interfaces that utilize voice and visuals. The data entered by the user is processed by the device's emotion analysis engine. In this process, voice and facial expression data are analyzed to recognize the user's emotional state.

[0556] The analyzed data is sent to a server. The server uses an emotion analysis engine and a generative AI model to integrate health data and emotion data and generate personalized prescription advice. The generated advice is sent to the device in a format that is sensitive to the user's emotions.

[0557] The device provides the user with received advice. The advice is presented in an appropriate tone using voice and visuals, designed to reduce the user's anxiety and stress. The device also manages the user's medication schedule and provides notifications that are sensitive to the user's emotional state, ensuring medication adherence.

[0558] This allows the system to provide personalized advice and plan management based on the user's emotions and health status, thereby supporting the user's well-being.

[0559] As a concrete example, if a user is feeling anxious about taking medication, the device will sense this emotion, and the server will generate specific advice to alleviate it. For example, it might offer a suggestion in a calm tone, such as, "It's a good idea to drink plenty of water when taking your medication." An example of a prompt might be, "Please evaluate the user's current emotional state based on their voice data and text analysis. If anxiety is detected, please suggest specific medication advice to alleviate it."

[0560] Thus, this invention aims to realize flexible medication management that comprehensively considers the user's psychological state and health condition.

[0561] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0562] Step 1:

[0563] Users input health data using a terminal. This data includes current medication information, changes in physical condition, and medical history. This health data is used by the system as basic information to understand the user's condition.

[0564] Step 2:

[0565] The device collects voice and visual information along with the user's health data. It uses the built-in microphone and camera to record the user's facial expressions and voice tone. Using this data, the emotion analysis engine identifies the user's emotional state (e.g., anxiety, reassurance).

[0566] Step 3:

[0567] The device transmits collected health and emotional data to the server. This transmitted data is integrated on the server side and becomes input information to understand the user's overall state.

[0568] Step 4:

[0569] The server analyzes the received health information and emotional data. Based on the emotional analysis results, a generative AI model generates prescription advice tailored to the user. For example, if the user is experiencing anxiety, advice is generated that takes that emotion into account and promotes a sense of security.

[0570] Step 5:

[0571] The server sends the generated advice to the terminal. The terminal then displays the received advice to the user. In this process, speech synthesis technology and a display are used to provide the advice in a tone that matches the user's emotional state.

[0572] Step 6:

[0573] The device manages the user's medication schedule based on generated advice. A reminder function is used to notify the user to take their medication at the appropriate time. Notifications are customized according to the user's preferences.

[0574] Step 7:

[0575] Users input feedback into the device about changes in their physical condition and emotions after taking medication. This feedback serves as important data for the system to improve the accuracy of future prescription advice.

[0576] Step 8:

[0577] The server receives feedback from users and updates its sentiment database. This updated data is then used in subsequent analyses and advice generation. Through this process, the system continuously evolves, enabling it to provide more appropriate advice.

[0578] (Application Example 2)

[0579] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0580] In modern society, users often feel anxious about choosing and using medications at pharmacies. Furthermore, accurately understanding a user's psychological state and providing personalized, effective medication advice is difficult in face-to-face interactions. In this situation, there is a need for a system that can respond appropriately to users' emotions.

[0581] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0582] In this invention, the server includes an information input means for inputting the user's health information, an advice generation means for analyzing the input health information and generating personalized drug prescription advice using generative artificial intelligence, an advice provision means for providing the generated advice to the user, an emotion analysis means for analyzing the user's emotional information and providing information in an appropriate tone according to the emotion, and a display means for maintaining the user's emotional state in a face-to-face environment and providing information to the service provider. This enables accurate understanding of the user's psychological state during face-to-face service at a pharmacy and allows for personalized and effective advice.

[0583] "Information input means" refers to a device or method for acquiring health-related information collected from users as digital data.

[0584] "Advice generation means" refers to a device or method for generating individually optimized drug prescription advice using generative artificial intelligence based on the user's health information.

[0585] "Advice provision means" refers to a device or method for providing generated advice to a user in an appropriate format.

[0586] "Emotion analysis means" refers to a device or method that has the function of identifying and analyzing emotions from a user's voice, facial expressions, etc.

[0587] "Display means" refers to a device or part of a device that visually represents analyzed information or generated advice and provides it to the customer.

[0588] A system for carrying out this invention includes information input means, advice generation means, advice provision means, emotion analysis means, and display means.

[0589] The server collects health information provided by the user through an information input method. This information input method utilizes hardware devices such as smart glasses or voice input devices to acquire the user's voice and image data in digital format. The collected data is stored on a cloud server and used for analysis.

[0590] Next, the server uses generative artificial intelligence to generate personalized medication prescription advice through an advice generation mechanism. The generative AI cross-references various health databases to generate advice best suited to the user's health condition. This enables the recommendation of medications tailored to the user's specific circumstances.

[0591] Furthermore, emotion analysis techniques are used to analyze the user's emotional state from their voice and facial expression data. Specifically, existing emotion recognition software (e.g., Affectiva or IBM Watson) can be used to understand the user's psychological state in real time.

[0592] The generated advice and emotion analysis results are provided to the service provider through display devices such as smart glasses. This allows the service provider to provide appropriate advice according to the user's emotions, enabling conversations that enhance the user's sense of security.

[0593] For example, if a user visits with concerns about medication side effects, sentiment analysis software detects the anxiety, and generative artificial intelligence generates reassuring information. The display system allows staff to explain to the user in an appropriate tone, "This medication has few side effects and is safe." An example of a prompt might be: "The customer is concerned about medication side effects. Please generate reassuring and gentle persuasive advice."

[0594] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0595] Step 1:

[0596] The server receives user health information and voice / visual data through the terminal. This input data includes the user's self-reported health status, medication history, voice tone, and facial expressions. The server stores this data digitally and integrates it into the initial database.

[0597] Step 2:

[0598] The server passes the received audio and visual data to an emotion analysis system to analyze the user's emotional state. The input data is processed in real time by emotion recognition software to identify the user's possible emotions (anxiety, relief, stress). This result is temporarily stored as intermediate data.

[0599] Step 3:

[0600] The server analyzes health information and generates personalized medication prescription advice using a generative AI model. This input includes the user's health status data, and the generative AI model, referencing a database, generates information on the most suitable medications for the user. The output advice is linked to the user's associated emotional data.

[0601] Step 4:

[0602] The terminal receives generated advice and sentiment analysis results sent from the server. This input information is transmitted to the display device and visually shown to the service provider in real time. The displayed information helps the service provider provide appropriate explanations to the user.

[0603] Step 5:

[0604] Customer service staff provide personalized medication advice to users based on information obtained from display devices. This approach takes into account the user's emotional state, and the information displayed is adjusted to provide a sense of reassurance.

[0605] Step 6:

[0606] Users input feedback into their devices, and the server receives this information. This feedback includes changes in the user's emotions and new health information. The server updates its database and uses this information for future analysis and training of generative AI models.

[0607] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0608] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0609] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0610] [Fourth Embodiment]

[0611] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0612] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0613] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0614] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0615] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0616] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0617] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0618] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0619] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0620] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0621] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0622] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0623] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0624] This invention relates to a medication advice system using an AI robot installed in the home. This system supports appropriate medication management by providing personalized medication advice based on the user's health information.

[0625] 1. User interface operation:

[0626] Users can access the system through their devices and input their health information, medical history, and allergy information. This includes voice input and text input using a touch panel.

[0627] 2. Analysis of health data:

[0628] The user's health information entered from the terminal is collected by the server. The server analyzes this information and uses generative artificial intelligence to generate optimal medication prescription advice for the user. This advice includes appropriate dosage, warnings about side effects, and risks of drug interactions.

[0629] 3. Providing advice:

[0630] The generated advice is sent from the server to the terminal. The terminal displays the advice to the user and provides audio explanations as needed. This allows the user to receive clear instructions on their own device.

[0631] 4. Managing medication schedules:

[0632] Based on a medication plan generated by the server, the device manages the user's medication schedule. As the time to take medication approaches, the device prompts the user to take their medication with voice and visual alerts.

[0633] 5. Feedback and data updates:

[0634] After taking medication, users input changes in their physical condition and new health information into their device and send it to the server. The server updates the data based on this feedback and incorporates it into future advice. This cycle ensures that the system always provides the latest and most personalized support.

[0635] Specific example:

[0636] For example, if a user suffering from high blood pressure uses the system, they input their medical history and current medications. The server analyzes this information and suggests new medications and dosages. Furthermore, based on the advice, it creates a medication schedule tailored to the user's schedule, such as before breakfast or before bed, allowing them to easily integrate their medication into their daily life.

[0637] This system supports users in taking medications tailored to their individual health conditions, thereby contributing to their overall health maintenance.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The user enters their health information into the terminal. Input methods include voice input or text input via a touchscreen. The terminal converts this information into text data and then into a format suitable for transmission to the server.

[0641] Step 2:

[0642] The server receives the user's health information sent from the terminal. The server stores this information in a database and prepares it for analysis. Simultaneously, it also performs encryption to protect privacy.

[0643] Step 3:

[0644] The server uses stored information and generative artificial intelligence to generate prescription advice tailored to the user. This includes selecting medications that take into account the user's medical history and allergy information, as well as warnings about side effects.

[0645] Step 4:

[0646] The server sends the generated advice to the terminal. The terminal prepares to present the received information to the user. The information can be displayed not only on the screen but also through voice guidance.

[0647] Step 5:

[0648] The device creates a medication schedule based on the generated advice. It sets medication times that fit the user's lifestyle and provides a function to notify the user of the schedule.

[0649] Step 6:

[0650] Users can take medication according to the advice given and report any changes in their physical condition or side effects they experience after taking it to their device. This provides feedback information that helps improve the quality of future advice.

[0651] Step 7:

[0652] The terminal processes user feedback information and sends it to the server. The server reflects this information in its database and updates the data for use in generating advice the next time. This cycle ensures that the system always reflects the latest information and can provide highly accurate advice.

[0653] (Example 1)

[0654] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0655] In modern society, personalized health management is in demand, but traditional methods have the challenge of not being able to properly prescribe medications and manage schedules according to the individual user's information. This problem is particularly serious for people who use a wide variety of medications, as incorrect use can lead to health problems.

[0656] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0657] In this invention, the server includes information acquisition means for inputting the user's physical condition information, instruction generation means for analyzing the input physical condition information and generating personalized drug prescription instructions using generative intelligence, and instruction presentation means for presenting the generated instructions to the user. This makes it possible to provide the user with the most suitable drug prescription and appropriate medication management.

[0658] "Information acquisition means" refers to a device or method for inputting information about a user's physical condition.

[0659] "Instruction generation means" refers to a device or method for analyzing input physical condition information and generating personalized drug prescription instructions using generative intelligence.

[0660] "Instruction presentation means" refers to a device or method for presenting prescription instructions for a generated drug to the user.

[0661] "Schedule management means" refers to a device or method for managing a user's medication schedule based on the prescription instructions for the generated drugs and for notifying the user of the schedule.

[0662] "Information update means" refers to a device or method for collecting additional information and opinions from users and updating the memory area.

[0663] "Generative intelligence" is an artificial intelligence technology that has the ability to generate optimal drug prescription instructions based on input data.

[0664] This invention relates to a drug administration instruction system using an artificial intelligence-equipped device installed in the home. This system provides personalized drug instructions based on the user's physical condition information, supporting appropriate medication management.

[0665] Users can access the system through terminals equipped with voice input or touch panels. The terminals collect information on the user's physical condition, medical history, and allergies, and send this information to the server. The server analyzes this data and uses a generative AI model to generate medication recommendations optimized for each individual user.

[0666] Instructions generated by the server are sent to the terminal and provided to the user. The terminal visually displays the generated instructions on the screen, and can also provide instructions by voice if the user has difficulty accessing visual information. This allows users to receive personalized instructions based on their health condition from the comfort of their homes.

[0667] As a concrete example, a user suffering from high blood pressure enters their blood pressure readings and information about medications they are currently taking into the terminal. Based on this information, the server generates specific instructions such as "Take medication X twice a day, in the morning and at night," and presents them to the user. The terminal then issues an alert at the specified time to prompt the user to take their medication.

[0668] As an example of a prompt, you could use a text-based instruction such as, "We want to develop an AI system that takes user health information and generates personalized medication recommendations. What kind of prompts would be effective?"

[0669] In this way, the system of the present invention can efficiently perform individual health management and contribute to maintaining the health of users.

[0670] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0671] Step 1:

[0672] Users input their physical condition, medical history, and allergy information using a terminal. Input methods include text input via a touch panel and voice input via voice recognition. This input data is important as basic information for understanding the user's health status. The terminal transmits this information to the server sequentially.

[0673] Step 2:

[0674] The server receives physical condition information sent from the terminal, compares it with the database, and performs analysis. Using a generative AI model, it analyzes the user's information in detail and generates personalized medication prescriptions. This generation process takes into account the input data, considering appropriate medication types, dosages, and side effect information.

[0675] Step 3:

[0676] The generated medication instructions are sent from the server to the terminal. The terminal displays the instructions visually on the screen and, if necessary, also provides them audibly. This allows the user to check medication information tailored to their needs in an intuitively understandable format. For example, instructions such as "Take one tablet of medication X after breakfast and one tablet after dinner" are output.

[0677] Step 4:

[0678] The device automatically creates and manages the user's medication schedule based on instructions sent from the server. As the designated medication time approaches, it notifies the user with an alert sound and screen notification. This feature helps prevent users from forgetting to take their medication, and provides support by notifying them with a voice message saying, "It's time to take your medicine."

[0679] Step 5:

[0680] Users input feedback into their device regarding changes in their physical condition after taking medication and any new observations they may have. This feedback information is sent to the server as data to be used in generating advice for the next time. The server updates its database based on the received feedback and incorporates it into the AI ​​model to provide more appropriate advice.

[0681] (Application Example 1)

[0682] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0683] Managing users' medication adherence presents challenges, particularly in preventing missed doses or incorrect dosages while out and about. This is especially true in public places or during travel, where users are more likely to forget to take their medication properly. Furthermore, real-time monitoring of users' health information and updating medication plans accordingly are essential. Addressing these challenges and supporting proper medication management and adherence is crucial.

[0684] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0685] In this invention, the server includes data input means for inputting the user's health information, advice generation means for analyzing the input health information and generating personalized medication prescription advice using generative artificial intelligence, data synchronization means for synchronizing health data to the cloud, and information provision means for providing the generated advice to the user. This allows the user to receive an appropriate medication schedule even when away from home, preventing medication errors.

[0686] "Data input means" refers to devices or methods for users to input their health information into a system.

[0687] An "advice generation means" is a device or method that uses generative artificial intelligence to create personalized drug prescription advice based on input health information.

[0688] "Data synchronization means" refers to devices or methods for updating and aligning health information on the cloud.

[0689] "Information provision means" refers to devices or methods that provide generated advice to users in an easily understandable format.

[0690] A "notification management system" is a device or method that manages a user's medication plan based on generated advice and provides notifications at the appropriate time.

[0691] "Information update means" refers to devices and methods for collecting additional information and feedback from users and keeping the information infrastructure constantly up-to-date.

[0692] This invention is a system for users to manage their health information and receive personalized medication advice. The server acquires health information from the user through a data input means. This health information is entered via the user's smartphone, tablet, or other device. For voice input, the system utilizes a "voice recognition API" on smart devices to convert the entered information into text in real time.

[0693] Next, the server uses an advice generation mechanism to analyze the collected health information and generates optimal medication prescription advice for the user using a generation AI model. This generation utilizes a "processing service" in a cloud computing environment, leveraging high-performance computing capabilities to provide fast and accurate prescription advice.

[0694] Furthermore, the generated advice is synchronized on the cloud and kept up-to-date at all times through data synchronization mechanisms. This information is sent to the user's device and provided to the user via audio and visual means through information delivery mechanisms. This ensures that users can always obtain accurate medication information no matter where they are.

[0695] For example, if a user is out and about and it's time to take their medication, the device will send a reminder via the notification management system. This allows the user to take their medication properly by following voice instructions, even in public places, without worrying about being seen by others.

[0696] Furthermore, post-medication feedback is sent to the cloud via an information update mechanism, keeping the database constantly up-to-date. This data is used by the server to generate advice for the next dose. An example of a prompt would be: "The user's latest blood glucose level is 150 mg / dL. Please generate advice for the next dose, taking into account meal timing and medication history." This prompt is used as input to an AI model, which generates a medication plan tailored to the individual's situation.

[0697] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0698] Step 1:

[0699] Users enter health information through their device. This information includes age, gender, medical history, allergy information, and current medications. This input is performed using a "voice recognition API" or touch panel and sent to the server.

[0700] Step 2:

[0701] The server receives health information acquired through data input and generates prompts for the generating AI model based on that information. These prompts include user-specific data necessary to provide appropriate medication advice to the user. After these prompts are generated, they are sent to the generating AI model.

[0702] Step 3:

[0703] The generation AI model generates optimal medication prescription advice for the user based on the received prompts. This process produces advice that includes specific dosages and precautions tailored to the user's individual health condition and lifestyle. The generated advice is returned to the server.

[0704] Step 4:

[0705] The server synchronizes newly generated advice to the cloud. During this process, data synchronization is used to ensure all advice data is up-to-date, preparing for future advice generation and user notifications.

[0706] Step 5:

[0707] Advice sent from the server is presented to the user via the terminal's information delivery system. The terminal uses audio output and display functions to allow the user to visually and audibly confirm the advice. This step supports how the user actually uses that advice.

[0708] Step 6:

[0709] Users take their medication based on the advice they receive and then input feedback into their device. This feedback includes the actual time of administration and any changes in their physical condition. This data is sent to a server, and the database is updated using an information update mechanism.

[0710] Step 7:

[0711] The server uses the updated data to prepare for generating the next set of advice. This allows for the optimization of medication management while consistently providing users with the most appropriate medication advice.

[0712] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0713] This invention relates to a home-use AI robot pharmacist system that includes an emotion engine. It not only personalizes medication prescription advice based on the user's health information but also considers the user's emotional state. This system optimizes the user's medication experience by comprehensively combining information input, emotion recognition, advice generation, and advice delivery functions.

[0714] 1. User Interface and Emotion Recognition:

[0715] When users input health information through their devices, their voice or visual responses are recorded. The device analyzes these responses and recognizes the user's emotions through an emotion engine. This allows the system to understand the user's psychological state (e.g., anxiety, reassurance, stress).

[0716] 2. Analysis of health data and emotional data:

[0717] The server receives health information and emotional data transmitted from the terminal. An emotion engine analyzes the user's emotions and combines them with health information for a comprehensive evaluation. Generative artificial intelligence then generates personalized prescription advice based on this data.

[0718] 3. Providing advice that is tailored to the individual's emotions:

[0719] The server sends the generated advice to the terminal, and the terminal provides the advice in a tone and content that suits the user's emotional state. For example, a user who is feeling anxious will receive a calm and reassuring explanation.

[0720] 4. Advice and Schedule Management:

[0721] The device manages the medication schedule while considering the user's emotional state and provides emotionally sensitive notifications. For example, reminders can be customized in the user's preferred voice or visual style.

[0722] 5. Feedback and data updates:

[0723] Users input feedback into their device about changes in their feelings and emotions after taking medication. The server receives the new emotional data, updates its database, and uses it for the next analysis.

[0724] Specific example:

[0725] If a user is using the system while feeling anxious about medication side effects, the terminal detects this anxiety from the user's tone of voice and words. Based on this information, the server generates advice to reduce the risk of side effects in order to provide reassurance. The terminal then delivers this advice in a quiet and calming tone to alleviate the user's anxiety.

[0726] Thus, the present invention aims to enable flexible responses that take into account the user's psychological state and to provide a better experience regarding medication use.

[0727] The following describes the processing flow.

[0728] Step 1:

[0729] Users input health information into the device via voice or text. During this process, the device also records the user's voice tone and word choices, collecting data for analysis by an emotion engine.

[0730] Step 2:

[0731] The device sends the user's health information and emotional data to the server. The emotion engine analyzes voice tone and facial expression data to identify the user's emotional state. For example, if the user is feeling anxious or stressed, it extracts that information.

[0732] Step 3:

[0733] The server integrates and analyzes received health information and emotional data. Using generative artificial intelligence, it generates personalized medication prescription advice that takes into account the user's emotional state. This advice includes wording tailored to the user's psychological condition.

[0734] Step 4:

[0735] The server sends the generated advice to the device. The device displays or provides the advice in a tone and language that suits the user's emotions. For users who are feeling anxious, the advice is explained in a relaxed tone, demonstrating an emotionally sensitive approach.

[0736] Step 5:

[0737] Users take medication based on the advice provided. After taking the medication, they input feedback into their device and report their emotional state after taking it. This provides data to improve future advice.

[0738] Step 6:

[0739] The device organizes user feedback and sends it to the server. The server records this information in a database, uses an emotion engine to update the data, and uses it to generate advice for the next time. This process ensures that the system always provides the best possible support to the user using the most up-to-date information.

[0740] (Example 2)

[0741] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0742] In modern medication management systems, providing a personalized medication experience that takes into account the user's emotional state is challenging. In particular, prescription advice and medication schedules often fail to reflect the user's psychological state, resulting in insufficient reduction of anxiety and stress.

[0743] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0744] In this invention, the server includes data acquisition means for receiving information about the user's health, emotion analysis means for analyzing the user's voice and images to recognize their emotional state, and advice generation means for integrating health information and emotion data and generating personalized prescription advice using a generation AI model. This enables the provision of flexible and personalized advice according to the user's emotional state and the management of medication schedules.

[0745] "Data acquisition means" refers to devices or methods that have the function of collecting health-related information from users.

[0746] "Emotional analysis means" refers to technologies and devices for identifying a user's emotional state from their voice or images.

[0747] A "generative AI model" is a machine learning model that generates personalized results based on specific input data.

[0748] An "advice generation means" is a device or method that has the function of creating prescription advice optimized for the user using acquired health information and emotional data.

[0749] "An advice delivery method" refers to a device or method for presenting generated advice in a way that is appropriate to the user's emotional state.

[0750] A "schedule management tool" refers to a device or method that has the function of creating a medication plan for a user and notifying them in an emotionally sensitive manner.

[0751] "Data update methods" refer to technologies and devices that update databases based on user feedback to improve analysis accuracy.

[0752] This invention is a system that provides personalized prescription advice using the user's health information and emotional state. The system primarily consists of terminals and a server, and by integrating these functions, it provides the user with an optimal medication management experience.

[0753] Specifically, the device provides an interface for users to input health-related data. This includes interfaces that utilize voice and visuals. The data entered by the user is processed by the device's emotion analysis engine. In this process, voice and facial expression data are analyzed to recognize the user's emotional state.

[0754] The analyzed data is sent to a server. The server uses an emotion analysis engine and a generative AI model to integrate health data and emotion data and generate personalized prescription advice. The generated advice is sent to the device in a format that is sensitive to the user's emotions.

[0755] The device provides the user with received advice. The advice is presented in an appropriate tone using voice and visuals, designed to reduce the user's anxiety and stress. The device also manages the user's medication schedule and provides notifications that are sensitive to the user's emotional state, ensuring medication adherence.

[0756] This allows the system to provide personalized advice and plan management based on the user's emotions and health status, thereby supporting the user's well-being.

[0757] As a concrete example, if a user is feeling anxious about taking medication, the device will sense this emotion, and the server will generate specific advice to alleviate it. For example, it might offer a suggestion in a calm tone, such as, "It's a good idea to drink plenty of water when taking your medication." An example of a prompt might be, "Please evaluate the user's current emotional state based on their voice data and text analysis. If anxiety is detected, please suggest specific medication advice to alleviate it."

[0758] Thus, this invention aims to realize flexible medication management that comprehensively considers the user's psychological state and health condition.

[0759] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0760] Step 1:

[0761] Users input health data using a terminal. This data includes current medication information, changes in physical condition, and medical history. This health data is used by the system as basic information to understand the user's condition.

[0762] Step 2:

[0763] The device collects voice and visual information along with the user's health data. It uses the built-in microphone and camera to record the user's facial expressions and voice tone. Using this data, the emotion analysis engine identifies the user's emotional state (e.g., anxiety, reassurance).

[0764] Step 3:

[0765] The device transmits collected health and emotional data to the server. This transmitted data is integrated on the server side and becomes input information to understand the user's overall state.

[0766] Step 4:

[0767] The server analyzes the received health information and emotional data. Based on the emotional analysis results, a generative AI model generates prescription advice tailored to the user. For example, if the user is experiencing anxiety, advice is generated that takes that emotion into account and promotes a sense of security.

[0768] Step 5:

[0769] The server sends the generated advice to the terminal. The terminal then displays the received advice to the user. In this process, speech synthesis technology and a display are used to provide the advice in a tone that matches the user's emotional state.

[0770] Step 6:

[0771] The device manages the user's medication schedule based on generated advice. A reminder function is used to notify the user to take their medication at the appropriate time. Notifications are customized according to the user's preferences.

[0772] Step 7:

[0773] Users input feedback into the device about changes in their physical condition and emotions after taking medication. This feedback serves as important data for the system to improve the accuracy of future prescription advice.

[0774] Step 8:

[0775] The server receives feedback from users and updates its sentiment database. This updated data is then used in subsequent analyses and advice generation. Through this process, the system continuously evolves, enabling it to provide more appropriate advice.

[0776] (Application Example 2)

[0777] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0778] In modern society, users often feel anxious about choosing and using medications at pharmacies. Furthermore, accurately understanding a user's psychological state and providing personalized, effective medication advice is difficult in face-to-face interactions. In this situation, there is a need for a system that can respond appropriately to users' emotions.

[0779] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0780] In this invention, the server includes an information input means for inputting the user's health information, an advice generation means for analyzing the input health information and generating personalized drug prescription advice using generative artificial intelligence, an advice provision means for providing the generated advice to the user, an emotion analysis means for analyzing the user's emotional information and providing information in an appropriate tone according to the emotion, and a display means for maintaining the user's emotional state in a face-to-face environment and providing information to the service provider. This enables accurate understanding of the user's psychological state during face-to-face service at a pharmacy and allows for personalized and effective advice.

[0781] "Information input means" refers to a device or method for acquiring health-related information collected from users as digital data.

[0782] "Advice generation means" refers to a device or method for generating individually optimized drug prescription advice using generative artificial intelligence based on the user's health information.

[0783] "Advice provision means" refers to a device or method for providing generated advice to a user in an appropriate format.

[0784] "Emotion analysis means" refers to a device or method that has the function of identifying and analyzing emotions from a user's voice, facial expressions, etc.

[0785] "Display means" refers to a device or part of a device that visually represents analyzed information or generated advice and provides it to the customer.

[0786] A system for carrying out this invention includes information input means, advice generation means, advice provision means, emotion analysis means, and display means.

[0787] The server collects health information provided by the user through an information input method. This information input method utilizes hardware devices such as smart glasses or voice input devices to acquire the user's voice and image data in digital format. The collected data is stored on a cloud server and used for analysis.

[0788] Next, the server uses generative artificial intelligence to generate personalized medication prescription advice through an advice generation mechanism. The generative AI cross-references various health databases to generate advice best suited to the user's health condition. This enables the recommendation of medications tailored to the user's specific circumstances.

[0789] Furthermore, emotion analysis techniques are used to analyze the user's emotional state from their voice and facial expression data. Specifically, existing emotion recognition software (e.g., Affectiva or IBM Watson) can be used to understand the user's psychological state in real time.

[0790] The generated advice and emotion analysis results are provided to the service provider through display devices such as smart glasses. This allows the service provider to provide appropriate advice according to the user's emotions, enabling conversations that enhance the user's sense of security.

[0791] For example, if a user visits with concerns about medication side effects, sentiment analysis software detects the anxiety, and generative artificial intelligence generates reassuring information. The display system allows staff to explain to the user in an appropriate tone, "This medication has few side effects and is safe." An example of a prompt might be: "The customer is concerned about medication side effects. Please generate reassuring and gentle persuasive advice."

[0792] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0793] Step 1:

[0794] The server receives user health information and voice / visual data through the terminal. This input data includes the user's self-reported health status, medication history, voice tone, and facial expressions. The server stores this data digitally and integrates it into the initial database.

[0795] Step 2:

[0796] The server passes the received audio and visual data to an emotion analysis system to analyze the user's emotional state. The input data is processed in real time by emotion recognition software to identify the user's possible emotions (anxiety, relief, stress). This result is temporarily stored as intermediate data.

[0797] Step 3:

[0798] The server analyzes health information and generates personalized medication prescription advice using a generative AI model. This input includes the user's health status data, and the generative AI model, referencing a database, generates information on the most suitable medications for the user. The output advice is linked to the user's associated emotional data.

[0799] Step 4:

[0800] The terminal receives generated advice and sentiment analysis results sent from the server. This input information is transmitted to the display device and visually shown to the service provider in real time. The displayed information helps the service provider provide appropriate explanations to the user.

[0801] Step 5:

[0802] Customer service staff provide personalized medication advice to users based on information obtained from display devices. This approach takes into account the user's emotional state, and the information displayed is adjusted to provide a sense of reassurance.

[0803] Step 6:

[0804] Users input feedback into their devices, and the server receives this information. This feedback includes changes in the user's emotions and new health information. The server updates its database and uses this information for future analysis and training of generative AI models.

[0805] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0806] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0807] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0808] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0809] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0810] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0811] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0812] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0813] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0814] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0815] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0816] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0817] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0818] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0819] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0820] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0821] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0822] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0823] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0824] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0825] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0826] The following is further disclosed regarding the embodiments described above.

[0827] (Claim 1)

[0828] A means of inputting information for entering the user's health information,

[0829] An advice generation method that analyzes input health information and generates personalized drug prescription advice using generative artificial intelligence,

[0830] An advice provision method that provides the generated advice to the user,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, comprising a schedule management means for managing the user's medication schedule based on generated advice and notifying the user.

[0834] (Claim 3)

[0835] The system according to claim 1, comprising data update means for collecting additional information and feedback from users and updating the database.

[0836] "Example 1"

[0837] (Claim 1)

[0838] A means for acquiring information to input the user's physical condition information,

[0839] An instruction generation means that analyzes input physical condition information and generates personalized drug prescription instructions using generative intelligence,

[0840] An instruction presentation means that presents the generated instructions to the user,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, comprising a schedule management means for managing the user's medication schedule based on generated instructions and notifying the user.

[0844] (Claim 3)

[0845] The system according to claim 1, comprising information update means for collecting additional information and opinions from users and updating the memory area.

[0846] "Application Example 1"

[0847] (Claim 1)

[0848] A data entry method for inputting user health information,

[0849] An advice generation method that analyzes input health information and generates personalized drug prescription advice using generative artificial intelligence,

[0850] A data synchronization method for synchronizing health data on the cloud,

[0851] An information provision method that provides the generated advice to the user,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, comprising a notification management means for managing the user's medication plan based on generated advice and notifying the user.

[0855] (Claim 3)

[0856] The system according to claim 1, comprising information update means for collecting additional information and feedback from users and updating the information infrastructure.

[0857] "Example 2 of combining an emotion engine"

[0858] (Claim 1)

[0859] A means of acquiring data to receive information about the user's health,

[0860] An emotion analysis method that analyzes the user's voice and images to recognize their emotional state,

[0861] An advice generation method that integrates health information and emotional data and generates personalized prescription advice using a generation AI model,

[0862] An advice delivery method that provides generated advice in a tone that matches the user's emotional state,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, comprising a scheduling means for managing the user's medication plan based on generated advice and providing notifications in a manner that respects the user's feelings.

[0866] (Claim 3)

[0867] The system according to claim 1, comprising data updating means for collecting emotional feedback from users and updating a database based on that feedback to improve analysis accuracy.

[0868] "Application example 2 when combining with an emotional engine"

[0869] (Claim 1)

[0870] A means of inputting information for entering the user's health information,

[0871] An advice generation means that analyzes input health information and generates personalized drug prescription advice using generative artificial intelligence,

[0872] An advisory provision means that provides the generated advice to the user,

[0873] A sentiment analysis tool that analyzes user emotional information and provides information in an appropriate tone according to that emotion,

[0874] A display means that maintains the user's emotional state in a face-to-face environment and provides information to the service provider,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, comprising a scheduling means for managing the user's medication schedule based on generated advice and notifying the user.

[0878] (Claim 3)

[0879] The system according to claim 1, comprising data update means for collecting additional information and feedback from users and updating the database, and processing means for analyzing and reflecting emotional information in real time. [Explanation of symbols]

[0880] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting information for entering the user's health information, An advice generation method that analyzes input health information and generates personalized drug prescription advice using generative artificial intelligence, An advice provision method that provides the generated advice to the user, A system that includes this.

2. The system according to claim 1, comprising a schedule management means for managing the user's medication schedule based on generated advice and notifying the user.

3. The system according to claim 1, comprising data update means for collecting additional information and feedback from users and updating the database.

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